README.md

metadata

language:
  - en
license: cc-by-4.0
size_categories:
  - 10K<n<100K
task_categories:
  - time-series-forecasting
  - tabular-classification
tags:
  - finance
  - stock-market
  - technical-indicators
  - time-series
  - trading
  - OHLCV
pretty_name: Hourly Stock Prices with Technical Indicators (2023)

Hourly Stock Prices + Technical Indicators (2023)

This dataset contains hourly OHLCV price data and key technical indicators for 8 major U.S. tickers across different sectors. Perfect for time series forecasting, technical analysis, and machine learning projects.

Coverage: January 3, 2023 – December 18, 2023
Symbols: AAPL, MSFT, NVDA, JPM, XOM, SPY, TSLA, AMZN
Records: 11,202
Size: 2.16 MB

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📊 Columns

Column	Description
timestamp	Date & time in UTC (YYYY-MM-DD HH:MM:SS)
symbol	Stock ticker
open, high, low, close, volume	OHLCV data
sma_10, sma_50	Simple moving averages
ema_20	Exponential moving average
rsi_14	Relative Strength Index
macd, macd_signal, macd_hist	MACD components
volatility_20	Rolling volatility (20-hour window)
target_up_next	Binary target: 1 if next hour close ≥ 0.05% higher

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⚙️ Technical Details

• Data source: Publicly available financial market data (2023), aggregated and preprocessed to include technical indicators and binary movement labels.
• Interval: 1 hour (aggregated from minute-level data)
• Technical indicators: Calculated using pandas with proper groupby operations per symbol
• Missing values: 16 rows (0.14%) in volatility_20 column - occurs at the start of each symbol's time series where insufficient history exists for 20-hour rolling window
• Timestamps: UTC format, ISO 8601 compliant (YYYY-MM-DD HH:MM:SS)
• Metadata: metadata.json contains full dataset generation details including date ranges, symbols, and target threshold

📈 Data Quality

• ✅ No duplicate records
• ✅ All prices positive and valid
• ✅ All volumes positive
• ✅ Timestamps properly formatted
• ✅ Target variable balanced (41.75% ups, 58.25% downs)

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🚀 Quick Start

Load from Hugging Face

from datasets import load_dataset
import pandas as pd

# Load dataset
dataset = load_dataset("YOUR_USERNAME/hourly-stock-data-2023")
df = pd.DataFrame(dataset['train'])

# Convert timestamp to datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])

print(df.head())

Direct CSV loading

import pandas as pd

df = pd.read_csv('hf://datasets/YOUR_USERNAME/hourly-stock-data-2023/hourly_stock_prices_technical_indicators.csv')
df['timestamp'] = pd.to_datetime(df['timestamp'])

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🧠 Example Usage

Load and explore

import pandas as pd

# Load dataset
df = pd.read_csv('hourly_stock_prices_technical_indicators.csv')
df['timestamp'] = pd.to_datetime(df['timestamp'])

# Basic statistics
print(f"Total records: {len(df):,}")
print(f"Symbols: {df['symbol'].nunique()}")
print(f"Date range: {df['timestamp'].min()} to {df['timestamp'].max()}")

# Target distribution per symbol
df.groupby('symbol')['target_up_next'].mean()

Time series analysis

# Filter for specific symbol
aapl = df[df['symbol'] == 'AAPL'].set_index('timestamp')

# Plot price with moving averages
import matplotlib.pyplot as plt
aapl[['close', 'sma_10', 'sma_50', 'ema_20']].plot(figsize=(12, 6))
plt.title('AAPL Price with Technical Indicators')
plt.show()